Introduction
This R Markdown report enables to analyze time series from Yahoo Finance of userâs choice. The report takes user input and creates tables, plots and tests based on that input and allows to thoroughly analyze different quotes and time spans without interfering with the code.
This version of report provides several tech and motor companies to choose from, however, should one want to analyze another, he/she should add the abbreviation and companyâs full name to the parameterâs choice list and dictionary and the report would be good to go. Default time range spans back a thousand days from the day of running the report. Should the multiple choice for the quotes be left empty, then as default, all prices are included in the analysis. Last parameter determines the forecast period in days.
Data
Gathering of the time series data is performed via getSymbols function provided in quantmod package. More information about the package can be found here. Data used in this particular report is characterized by following parameters:
- Company name:
Apple Inc. - Company abbreviation:
AAPL - Time span:
2018-09-02-2021-05-29 - Quotes:
None - Forecast:
0days
Below you can see a glimpse of the first and last few observations of the data of your choice.
| Date | AAPL.Open | AAPL.High | AAPL.Low | AAPL.Close | AAPL.Adjusted | AAPL.Volume |
|---|---|---|---|---|---|---|
| 2021-05-28 | 125.57 | 125.80 | 124.55 | 124.61 | 124.61 | 71 232 700 |
| 2021-05-27 | 126.44 | 127.64 | 125.08 | 125.28 | 125.28 | 94 625 600 |
| 2021-05-26 | 126.96 | 127.39 | 126.42 | 126.85 | 126.85 | 56 575 900 |
| 2021-05-25 | 127.82 | 128.32 | 126.32 | 126.90 | 126.90 | 72 009 500 |
| 2021-05-24 | 126.01 | 127.94 | 125.94 | 127.10 | 127.10 | 63 092 900 |
| 2021-05-21 | 127.82 | 128.00 | 125.21 | 125.43 | 125.43 | 79 209 300 |
| Date | AAPL.Open | AAPL.High | AAPL.Low | AAPL.Close | AAPL.Adjusted | AAPL.Volume |
|---|---|---|---|---|---|---|
| 2018-09-11 | 54.5025 | 56.0750 | 54.1400 | 55.9625 | 54.29848 | 142 996 000 |
| 2018-09-10 | 55.2375 | 55.4625 | 54.1175 | 54.5825 | 52.95951 | 158 066 000 |
| 2018-09-07 | 55.4625 | 56.3425 | 55.1775 | 55.3250 | 53.67994 | 150 479 200 |
| 2018-09-06 | 56.5575 | 56.8375 | 55.3250 | 55.7750 | 54.11656 | 137 160 000 |
| 2018-09-05 | 57.2475 | 57.4175 | 56.2750 | 56.7175 | 55.03103 | 133 332 000 |
| 2018-09-04 | 57.1025 | 57.2950 | 56.6575 | 57.0900 | 55.39246 | 109 560 400 |
Descriptive statistics
General
Below you can find a handful of descriptive statistics regarding the columns specified during knitting. Maximum price recorded for Apple Inc. during 2018-09-02 - 2021-05-29 period was $145.09, while the minimum was $31.82.
| Variable | Mean | Std. | Min | 1st quartile | Median | 3rd quartile | Max |
|---|---|---|---|---|---|---|---|
| AAPL.Open | 79.06 | 32.36 | 35.99 | 51.07 | 67.50 | 115.23 | 143.60 |
| AAPL.High | 80.02 | 32.74 | 36.43 | 51.53 | 69.31 | 116.55 | 145.09 |
| AAPL.Low | 78.08 | 31.82 | 35.50 | 50.72 | 66.82 | 113.61 | 141.37 |
| AAPL.Close | 79.08 | 32.27 | 35.55 | 51.12 | 68.31 | 115.08 | 143.16 |
| AAPL.Adjusted | 78.32 | 32.56 | 34.61 | 50.12 | 67.67 | 114.52 | 142.70 |
| AAPL.Volume | 133 179 276.20 | 60 331 655.95 | 45 448 000.00 | 91 717 600.00 | 116 736 000.00 | 157 125 200.00 | 426 510 000.00 |
Log returns
This chapter provides insight on the log returns of selected prices. Log returns are more widely used in finance than simple returns for a number of reasons, one being that prices are assumed to be log-normally distributed. That is not always the case as can be seen on the graphs below. Nevertheless, this assumption results in logarithms of prices to be normally distributed. More information on this topic can be found here.
Each of the graphs below represents a histogram of log returns of certain price, as well as kernel density estimate (white line). It is plotted agains a normal distribution (red line) with mean and standard deviation calculated separately for each graph.
AAPL.Open
AAPL.High
AAPL.Low
AAPL.Close
AAPL.Adjusted
Plotting
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Stationarity analysis
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Trend
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0316 -0.7813 0.1243 0.7799 9.3996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0065912 0.1500865 -0.044 0.96498
## z.lag.1 -1.1618669 0.0549135 -21.158 < 2e-16 ***
## tt 0.0003589 0.0003781 0.949 0.34294
## z.diff.lag 0.1155851 0.0380447 3.038 0.00247 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.959 on 682 degrees of freedom
## Multiple R-squared: 0.5271, Adjusted R-squared: 0.525
## F-statistic: 253.3 on 3 and 682 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -21.1581 149.2237 223.8355
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.96 -3.41 -3.12
## phi2 6.09 4.68 4.03
## phi3 8.27 6.25 5.34According to the ADF test the series are integrated of order 1, or in other words have become stationary after 1 differentiation(s). The program differentiated the series until it found order where value of test-statistic: -21.1581278 is smaller than tau3 5pct: -3.41.
Drift
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression drift
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.9871 -0.7769 0.0824 0.7793 9.4498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11679 0.07500 1.557 0.11989
## z.lag.1 -1.15936 0.05485 -21.138 < 2e-16 ***
## z.diff.lag 0.11432 0.03802 3.007 0.00274 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.959 on 683 degrees of freedom
## Multiple R-squared: 0.5264, Adjusted R-squared: 0.5251
## F-statistic: 379.6 on 2 and 683 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -21.1385 223.4177
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau2 -3.43 -2.86 -2.57
## phi1 6.43 4.59 3.78According to the ADF test the series are integrated of order 1, or in other words have become stationary after 1 differentiation(s). The program differentiated the series until it found order where value of test-statistic: -21.1384808 is smaller than tau3 5pct: -2.86.
None
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression none
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.9020 -0.6647 0.1942 0.8967 9.5563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## z.lag.1 -1.15303 0.05475 -21.059 < 2e-16 ***
## z.diff.lag 0.11114 0.03800 2.924 0.00356 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.961 on 684 degrees of freedom
## Multiple R-squared: 0.5248, Adjusted R-squared: 0.5234
## F-statistic: 377.6 on 2 and 684 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -21.0591
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau1 -2.58 -1.95 -1.62According to the ADF test the series are integrated of order 1, or in other words have become stationary after 1 differentiation(s). The program differentiated the series until it found order where value of test-statistic: -21.059126 is smaller than tau3 5pct: -1.95.
Forecasting?
ARIMA?
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